test = h5py.File(validation_file)
images = test['images'].value
labels = test['labels'].value
y_test = tf.keras.utils.to_categorical(labels, 7)
x_test = images/255
inputs = Input(shape=(112, 112, 3))
x = Conv2D(32, (3, 3), activation='relu')(inputs)
x = Conv2D(64, (3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.5)(x)
y = Dense(7, activation='softmax')(x)
model = Model(inputs=inputs, outputs=y)
model.compile(optimizer = Adadelta(),
loss='categorical_crossentropy',
metrics=['accuracy'])
tensorboard = TensorBoard(log_dir='../logs/{0}'.format(time()))
model.fit(x_train, y_train, batch_size=32, epochs=25, verbose=1, callbacks=[tensorboard])
model.save(model_file)
class FusionModel:
def __init__(self, config, load_weight_path=None, ab_loss='mse'):
img_shape = (config.IMAGE_SIZE, config.IMAGE_SIZE)
# Creating generator and discriminator
optimizer = Adam(0.00002, 0.5)
self.foreground_generator = instance_network(img_shape)
self.foreground_generator.compile(loss=[
constant_loss_dummy, constant_loss_dummy, constant_loss_dummy,
constant_loss_dummy, constant_loss_dummy, ab_loss
],
optimizer=optimizer)
self.fusion_discriminator = discriminator_network(img_shape)
self.fusion_discriminator.compile(loss=wasserstein_loss_dummy,
optimizer=optimizer)
self.fusion_generator = fusion_network(img_shape, config.BATCH_SIZE)
self.fusion_generator.compile(loss=[ab_loss, 'kld'],
optimizer=optimizer)
if load_weight_path:
chroma_gan = load_model(load_weight_path)
chroma_gan_layers = [layer.name for layer in chroma_gan.layers]
print('Loading chroma GAN parameter to instance network...')
instance_layer_names = [
layer.name for layer in self.foreground_generator.layers
]
for i, layer in enumerate(instance_layer_names):
if layer == 'fg_model_3':
print('model 3 skip')
continue
if len(layer) < 2:
continue
if layer[:3] == 'fg_':
try:
j = chroma_gan_layers.index(layer[3:])
self.foreground_generator.layers[i].set_weights(
chroma_gan.layers[j].get_weights())
print(f'Successfully set weights for layer {layer}')
except ValueError:
print(f'Layer {layer} not found in chroma gan.')
except Exception as e:
print(e)
print('Loading chroma GAN parameter to fusion network...')
fusion_layer_names = [
layer.name for layer in self.fusion_generator.layers
]
for i, layer in enumerate(fusion_layer_names):
if layer == 'model_3':
print('model 3 skip')
continue
try:
j = chroma_gan_layers.index(layer)
self.fusion_generator.layers[i].set_weights(
chroma_gan.layers[j].get_weights())
print(f'Successfully set weights for layer {layer}')
except ValueError:
print(f'Layer {layer} not found in chroma gan.')
except Exception as e:
print(e)
# Fg=instance prediction
fg_img_l = Input(shape=(*img_shape, 1, MAX_INSTANCES), name='fg_img_l')
# self.foreground_generator.trainable = False
self.foreground_generator.trainable = False
fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, up_sampling2d_3 = self.foreground_generator(
fg_img_l)
# Fusion prediction
fusion_img_l = Input(shape=(*img_shape, 1), name='fusion_img_l')
fusion_img_real_ab = Input(shape=(*img_shape, 2),
name='fusion_img_real_ab')
fg_bbox = Input(shape=(4, MAX_INSTANCES), name='fg_bbox')
fg_mask = Input(shape=(*img_shape, MAX_INSTANCES), name='fg_mask')
self.fusion_generator.trainable = False
fusion_img_pred_ab, fusion_class_vec = self.fusion_generator([
fusion_img_l, fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15,
fg_conv2d_17, fg_bbox, fg_mask
])
dis_pred_ab = self.fusion_discriminator(
[fusion_img_pred_ab, fusion_img_l])
dis_real_ab = self.fusion_discriminator(
[fusion_img_real_ab, fusion_img_l])
# Sample the gradient penalty
img_ab_interp_samples = RandomWeightedAverage()(
[fusion_img_pred_ab, fusion_img_real_ab])
dis_interp_ab = self.fusion_discriminator(
[img_ab_interp_samples, fusion_img_l])
partial_gp_loss = partial(
gradient_penalty_loss,
averaged_samples=img_ab_interp_samples,
gradient_penalty_weight=GRADIENT_PENALTY_WEIGHT)
partial_gp_loss.__name__ = 'gradient_penalty'
# Compile D and G as well as combined
self.discriminator_model = Model(
inputs=[
fusion_img_l, fusion_img_real_ab, fg_img_l, fg_bbox, fg_mask
],
outputs=[dis_real_ab, dis_pred_ab, dis_interp_ab],
name='discriminator')
self.discriminator_model.compile(optimizer=optimizer,
loss=[
wasserstein_loss_dummy,
wasserstein_loss_dummy,
partial_gp_loss
],
loss_weights=[-1.0, 1.0, 1.0])
self.fusion_generator.trainable = True
self.fusion_discriminator.trainable = False
self.combined = Model(
inputs=[fusion_img_l, fg_img_l, fg_bbox, fg_mask],
outputs=[
fusion_img_pred_ab, up_sampling2d_3, fusion_class_vec,
dis_pred_ab
],
name='combined')
self.combined.compile(
loss=[ab_loss, ab_loss, 'kld', wasserstein_loss_dummy],
loss_weights=[1.0, 0.5, 0.003, -0.1],
optimizer=optimizer)
# Monitor stuff
self.callback = TensorBoard(config.LOG_DIR)
self.callback.set_model(self.combined)
self.train_names = [
'loss', 'mse_loss', 'msei_loss', 'kullback_loss',
'wasserstein_loss'
]
self.disc_names = ['disc_loss', 'disc_valid', 'disc_fake', 'disc_gp']
self.test_loss_array = []
self.g_loss_array = []
def train(self,
data: Data,
test_data,
log,
config,
skip_to_after_epoch=None):
# Load VGG network
VGG_modelF = applications.vgg16.VGG16(weights='imagenet',
include_top=True)
# Real, Fake and Dummy for Discriminator
positive_y = np.ones((data.batch_size, 1), dtype=np.float32)
negative_y = -positive_y
dummy_y = np.zeros((data.batch_size, 1), dtype=np.float32)
# total number of batches in one epoch
total_batch = int(data.size / data.batch_size)
print(f'batch_size={data.batch_size} * total_batch={total_batch}')
save_path = lambda type, epoch: os.path.join(
config.MODEL_DIR, f"fusion_{type}Epoch{epoch}.h5")
if skip_to_after_epoch:
start_epoch = skip_to_after_epoch + 1
print(f"Loading weights from epoch {skip_to_after_epoch}")
self.combined.load_weights(
save_path("combined", skip_to_after_epoch))
self.fusion_discriminator.load_weights(
save_path("discriminator", skip_to_after_epoch))
else:
start_epoch = 0
for epoch in range(start_epoch, config.NUM_EPOCHS):
for batch in tqdm(range(total_batch)):
train_batch = data.generate_batch()
resized_l = train_batch.resized_images.l
resized_ab = train_batch.resized_images.ab
# GT vgg
predictVGG = VGG_modelF.predict(
np.tile(resized_l, [1, 1, 1, 3]))
# train generator
g_loss = self.combined.train_on_batch([
resized_l, train_batch.instances.l,
train_batch.instances.bbox, train_batch.instances.mask
], [
resized_ab, train_batch.instances.ab, predictVGG,
positive_y
])
# train discriminator
d_loss = self.discriminator_model.train_on_batch([
resized_l, resized_ab, train_batch.instances.l,
train_batch.instances.bbox, train_batch.instances.mask
], [positive_y, negative_y, dummy_y])
# update log files
write_log(self.callback, self.train_names, g_loss,
(epoch * total_batch + batch + 1))
write_log(self.callback, self.disc_names, d_loss,
(epoch * total_batch + batch + 1))
if batch % 10 == 0:
print(
f"[Epoch {epoch}] [Batch {batch}/{total_batch}] [generator loss: {g_loss[0]:08f}] [discriminator loss: {d_loss[0]:08f}]"
)
print('Saving models...')
self.combined.save(save_path("combined", epoch))
self.fusion_discriminator.save(save_path("discriminator", epoch))
print('Models saved.')
print('Sampling test images...')
# sample images after each epoch
self.sample_images(test_data, epoch, config)
def sample_images(self, test_data: Data, epoch, config):
total_batch = int(ceil(test_data.size / test_data.batch_size))
for _ in range(total_batch):
# load test data
test_batch = test_data.generate_batch()
# predict AB channels
fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, up_sampling2d_3 = self.foreground_generator.predict(
test_batch.instances.l)
fusion_img_pred_ab, _ = self.fusion_generator.predict([
test_batch.resized_images.l, fg_model_3, fg_conv2d_11,
fg_conv2d_13, fg_conv2d_15, fg_conv2d_17,
test_batch.instances.bbox, test_batch.instances.mask
])
# print results
for i in range(test_data.batch_size):
original_full_img = test_batch.images.full[i]
height, width, _ = original_full_img.shape
pred_ab = cv2.resize(
deprocess_float2int(fusion_img_pred_ab[i]),
(width, height))
reconstruct_and_save(
test_batch.images.l[i], pred_ab,
f'epoch{epoch}_{test_batch.file_names[i]}', config)
def train():
print(Messages.TRAINER_START)
data = []
labels = []
Trainer.insert_base_images()
for person in an_connector.persons:
for entry in database_connector.get(person.objectGUID):
image = ImageUtility.bin_to_np_arr(entry['data'])
data.append(image)
labels.append(person.objectGUID)
data = np.array(data, dtype='float32')
labels = np.array(labels)
lb = LabelBinarizer()
labels = lb.fit_transform(labels)
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
# If SSL exception occurs, run `Install Certificates.command` inside Applications/Python X
aug = ImageDataGenerator(rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2,
shear_range=0.15, horizontal_flip=True, fill_mode='nearest')
base_model = MobileNetV2(weights='imagenet', include_top=False, input_tensor=Input(shape=(224, 224, 3)))
head_model = base_model.output
head_model = AveragePooling2D(pool_size=(7, 7))(head_model)
head_model = Flatten(name='flatten')(head_model)
head_model = Dense(128, activation='relu')(head_model)
head_model = Dropout(0.5)(head_model)
head_model = Dense(len(labels), activation='softmax')(head_model)
model = Model(inputs=base_model.input, outputs=head_model)
# print(model.summary())
for layer in base_model.layers:
layer.trainable = False
print('Compiling model ... \n')
opt = Adam(lr=Trainer.INIT_LR, decay=Trainer.INIT_LR / Trainer.EPOCHS)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
# Needs depth of 3, received 1
print('Training head ... \n')
head = model.fit(x=aug.flow(trainX, trainY, batch_size=Trainer.BS), validation_data=(testX, testY),
steps_per_epoch=len(trainX) // Trainer.BS, epochs=Trainer.EPOCHS)
print('Evaluating network ... \n')
pred_idxs = model.predict(testX, batch_size=Trainer.BS)
pred_idxs = np.argmax(pred_idxs, axis=1)
# print(classification_report(testY.argmax(axis=1), pred_idxs, target_names=lb.classes_))
print('Saving mask detector model ... \n')
model.save('mask_detector.model', save_format='h5')
plt.style.use('ggplot')
plt.figure()
plt.plot(np.arange(0, Trainer.EPOCHS), head.history['loss'], label='train_loss')
plt.plot(np.arange(0, Trainer.EPOCHS), head.history['val_loss'], label='valuation_loss')
plt.plot(np.arange(0, Trainer.EPOCHS), head.history['accuracy'], label='train_acc')
plt.plot(np.arange(0, Trainer.EPOCHS), head.history['val_accuracy'], label='valuation_acc')
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch #')
plt.ylabel('Loss/Accuracy')
plt.legend(loc='lower left')
plt.savefig('plot.png')
print(Messages.TRAINER_FINISH)
x_train2, x_test2, y_train2, y_test2 = train_test_split(images,
races,
random_state=100)
inputs = Input(shape=(200, 200, 3))
flt = Flatten()(inputs)
gender_l = Dense(128, activation="relu")(flt)
gender_l = Dense(80, activation="relu")(gender_l)
gender_l = Dense(64, activation="relu")(gender_l)
gender_l = Dense(32, activation="relu")(gender_l)
gender_l = Dense(2, activation="softmax")(gender_l)
race_l = Dense(128, activation="relu")(flt)
race_l = Dense(80, activation="relu")(race_l)
race_l = Dense(64, activation="relu")(race_l)
race_l = Dense(32, activation="relu")(race_l)
race_l = Dense(7, activation="softmax")(race_l)
model = Model(inputs=inputs, outputs=[gender_l, race_l])
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics='accuracy')
csv_logger = CSVLogger("plots/AWE.csv", append=True)
save = model.fit(x_train, [y_train, y_train2],
validation_data=(x_test, [y_test, y_test2]),
epochs=100,
callbacks=[csv_logger])
model.save("AWEdataset.h5")
